中国物理B ›› 2013, Vol. 22 ›› Issue (2): 29302-029302.doi: 10.1088/1674-1056/22/2/029302

• GEOPHYSICS, ASTRONOMY, AND ASTROPHYSICS • 上一篇    下一篇

Estimation of lower refractivity uncertainty from radar sea clutter using Bayesian-MCMC method

盛峥   

  1. College of Meteorology and Oceangraphy, PLA University of Science and Technology, Nanjing 211101, China
  • 收稿日期:2012-05-07 修回日期:2012-07-04 出版日期:2013-01-01 发布日期:2013-01-01
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant No. 41105013); the National Natural Science Foundation of Jiangsu Province, China (Grant No. BK2011122); the Open Issue Foundation of Key Laboratory of Meteorological Disaster of Ministry of Education, China (Grant No. KLME1109); and the City Meteorological Scientific Research Fund, China (Grant No. IUMKY&UMRF201111).

Estimation of lower refractivity uncertainty from radar sea clutter using Bayesian-MCMC method

Sheng Zheng (盛峥)   

  1. College of Meteorology and Oceangraphy, PLA University of Science and Technology, Nanjing 211101, China
  • Received:2012-05-07 Revised:2012-07-04 Online:2013-01-01 Published:2013-01-01
  • Contact: Sheng Zheng E-mail:19994035@sina.com
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant No. 41105013); the National Natural Science Foundation of Jiangsu Province, China (Grant No. BK2011122); the Open Issue Foundation of Key Laboratory of Meteorological Disaster of Ministry of Education, China (Grant No. KLME1109); and the City Meteorological Scientific Research Fund, China (Grant No. IUMKY&UMRF201111).

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摘要： Estimation of lower atmospheric refractivity from radar sea clutter (RFC) is a complicated nonlinear optimization problem. This paper deals with the RFC problem in a Bayesian framework. It uses unbiased Markov Chain Monte Carlo (MCMC) sampling technique, which can provide accurate posterior probability distributions of the estimated refractivity parameters by using an electromagnetic split-step fast Fourier transform terrain parabolic equation propagation model within a Bayesian inversion framework. In contrast to the global optimization algorithm, the Bayesian-MCMC can obtain not only the approximate solutions, but also the probability distributions of the solutions, that is, uncertainty analyses of solutions. The Bayesian-MCMC algorithm is implemented on the simulation radar sea-clutter data and the real radar sea-clutter data. Reference data are assumed to be simulation data and refractivity profiles obtained with a helicopter. Inversion algorithm is assessed (i) by comparing the estimated refractivity profiles from the assumed simulation and the helicopter sounding data; (ii) the one-dimensional (1D) and two-dimensional (2D) posterior probability distribution of solutions.

关键词: refractivity from clutter, terrain parabolic equation propagation model, Bayesian-Markov chain Monte Carlo, uncertainty analysis

Abstract: Estimation of lower atmospheric refractivity from radar sea clutter (RFC) is a complicated nonlinear optimization problem. This paper deals with the RFC problem in a Bayesian framework. It uses unbiased Markov Chain Monte Carlo (MCMC) sampling technique, which can provide accurate posterior probability distributions of the estimated refractivity parameters by using an electromagnetic split-step fast Fourier transform terrain parabolic equation propagation model within a Bayesian inversion framework. In contrast to the global optimization algorithm, the Bayesian-MCMC can obtain not only the approximate solutions, but also the probability distributions of the solutions, that is, uncertainty analyses of solutions. The Bayesian-MCMC algorithm is implemented on the simulation radar sea-clutter data and the real radar sea-clutter data. Reference data are assumed to be simulation data and refractivity profiles obtained with a helicopter. Inversion algorithm is assessed (i) by comparing the estimated refractivity profiles from the assumed simulation and the helicopter sounding data; (ii) the one-dimensional (1D) and two-dimensional (2D) posterior probability distribution of solutions.

Key words: refractivity from clutter, terrain parabolic equation propagation model, Bayesian-Markov chain Monte Carlo, uncertainty analysis

中图分类号:  (Exploration of oceanic structures)

  • 93.85.Ly
41.20.Jb (Electromagnetic wave propagation; radiowave propagation)